Video tracking for high-similarity drug tablets based on reflective lightness intensity and fuzzy recognition system

Abstract

Video tracking of drug tablet exerts important influences on the efficiency and reliability of its mass production; this topic also becomes a difficult and targeted focus for pharmaceutical production monitory in the past several years due to the high similarity and random distribution of those objectives to be searched for. By measuring the reflective lightness intensity of illumination lightness on tablet surface, reflective lightness intensity matrix was established and demonstrated in the form of grey image, presenting its shape topology and topography details in return. On this basis, a series of mathematical properties for describing reflective lightness intensity images were proposed, thereafter a set of fuzzy recognition system and its identification rules can be employed to classify those moving tablets with inputted image properties, which facilitates the determination of their instantaneous coordinate positions on given image frame accordingly. By repeating identical operations on the next frame, the real-time motions of tablet objectives were traced successfully. Orthogonal tracking experiment and performance comparisons verified the accuracy and reliability of this new method in pharmaceutical industry. With original suggestions concerning imaging arrangements, tablet descriptions and video tracking, this article provides reliable references and new research ideas for tablet tracking performance in high-yielding production domain.

Hyperspectral Prediction Model of Soil Organic Matter in Black Soil Region Based on Fuzzy Recognition

“…The real-time motion of the tablet target is successfully tracked by repeating the same operation in the next frame. The accuracy and reliability of the method in the pharmaceutical industry were verified by orthogonal tracking experiments and performance comparison [20].…”

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Fuzzy prediction of AWJ turbulence characteristics by using typical multi-phase flow models

“…Figure 12 demonstrates the detailed arrangements of two exampled membership functions, as the waterjet pressure (input) was illustrated by Figure 12(a) and partitioned from 0 to 8, or the turbulence intensity (output) in Figure 12(b), in which any value beyond the presupposed range is infinity or zero. 0 was assumed as 'Very Low' and 8 as 'Very High,' other value levels can be identified by 'Low,' 'Medium,' and 'High' in sequence (Liang, Liu, Tan, & Wen, 2016).…”

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